• Title GANs in Action: Deep Learning with Generative Adversarial Networks
• Author(s) Jakub Langr and Vladimir Bok
• Publisher: Manning Publications; 1 edition (October 8, 2019)
• Permission: Free to read entire book online by the publisher (Manning), limited time every day.
• Paperback 240 pages
• eBook HTML
• Language: English
• ISBN-10: 1617295566
• ISBN-13: 978-1617295560
• Share This:  

Book Description

Generative Adversarial Networks (GANs) are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing." By pitting two neural networks against each other -- one to generate fakes and one to spot them -- GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems.

This book teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. You'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.

• Building your first GAN
• Handling the progressive growing of GANs
• Practical applications of GANs
• Troubleshooting your system

• Jakub Langr is working on ML tooling and was a Computer Vision Lead at Founders Factory.
• Vladimir Bok is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York-based startup.

• Amazon

• Neural Networks and Deep Learning
• Python Programming
• Machine Learning
• Artificial Intelligence
• Data Science

• GANs in Action: Deep Learning with Generative Adversarial Networks (Jakub Langr, et al)
• The Book Homepage

• Deep Learning (Ian Goodfellow, et al)
• Learning Deep Architectures for AI (Yoshua Bengio)
• Artificial Neural Networks (Bernhard Mehlig)
• Neural Networks and Deep Learning (Michael Nielsen)
• Deep Learning Tutorials (LISA Lab)